Metamers of neural networks reveal divergence from human perceptual systems

Deep neural networks have been embraced as models of sensory systems, instantiating representational transformations that appear to resemble those in the visual and auditory systems. To more thoroughly investigate their similarity to biological systems, we synthesized model metamers – stimuli that produce the same responses at some stage of a network’s representation. We generated model metamers for natural stimuli by performing gradient descent on a noise signal, matching the responses of individual layers of image and audio networks to a natural image or speech signal. The resulting signals reflect the invariances instantiated in the network up to the matched layer. We then measured whether model metamers were recognizable to human observers – a necessary condition for the model representations to replicate those of humans. Although model metamers from early network layers were recognizable to humans, those from deeper layers were not. Auditory model metamers became more human-recognizable with architectural modifications that reduced aliasing from pooling operations, but those from the deepest layers remained unrecognizable. We also used the metamer test to compare model representations. Cross-model metamer recognition dropped off for deeper layers, roughly at the same point that human recognition deteriorated, indicating divergence across model representations. The results reveal discrepancies between model and human representations, but also show how metamers can help guide model refinement and elucidate model representations.